Testing Identifying Assumptions in Bivariate Probit Models

Santiago Acerenza, Otavio Bartalotti and Desire Kedagni

ISU General Staff Papers from Iowa State University, Department of Economics

Abstract: This paper focuses on the bivariate probit model's identifyingassumptions: joint normality of errors, instrument exogeneity, and relevance conditions. First, we develop novel sharp testable equalities that can detect all possible observable violations of the assumptions. Second, we propose an easy-to-implement testing procedure for the model's validity based on feasible testable implications using existing inference methods for intersection bounds. The test achieves correct empirical size for moderately sized samples and performs well in detecting violations of the conditions in Monte Carlo simulations. Finally, we provide researchers with a road map on what to do when the bivariate probit model is rejected, including novel bounds for the average treatment effect that relax the normality assumption. Empirical examples illustrate the methodology's implementation.

Date: 2021-03-29
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://dr.lib.iastate.edu/server/api/core/bitstre ... f9a28f33a008/content
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Journal Article: Testing identifying assumptions in bivariate probit models (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:isu:genstf:202103290700001124

Access Statistics for this paper

More papers in ISU General Staff Papers from Iowa State University, Department of Economics Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070. Contact information at EDIRC.
Bibliographic data for series maintained by Curtis Balmer ().